diff options
Diffstat (limited to 'src/runtime/NEON/functions/NESoftmaxLayer.cpp')
-rw-r--r-- | src/runtime/NEON/functions/NESoftmaxLayer.cpp | 124 |
1 files changed, 40 insertions, 84 deletions
diff --git a/src/runtime/NEON/functions/NESoftmaxLayer.cpp b/src/runtime/NEON/functions/NESoftmaxLayer.cpp index 750992fca6..e763caa3a3 100644 --- a/src/runtime/NEON/functions/NESoftmaxLayer.cpp +++ b/src/runtime/NEON/functions/NESoftmaxLayer.cpp @@ -32,78 +32,41 @@ namespace arm_compute { template <bool IS_LOG> NESoftmaxLayerGeneric<IS_LOG>::NESoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(std::move(memory_manager)), _max_kernel(), _softmax_kernel(), _flat_or_reshape_ptr(nullptr), _fill_border_kernel(), _reshape(), _max(), _tmp(), _input_flattened(), _output_flattened(), - _needs_flattening(false) + : _memory_group(std::move(memory_manager)), _permute_input(), _permute_output(), _max_kernel(), _softmax_kernel(), _fill_border_kernel(), _max(), _tmp(), _input_permuted(), _output_permuted(), + _needs_permute(false) { } template <bool IS_LOG> -void NESoftmaxLayerGeneric<IS_LOG>::configure_reshape_input_kernel(const ITensor *input, const ITensor *output, int32_t first_n_reduce_axes) -{ - // Flatten the input - const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input->info(), first_n_reduce_axes); - - // Initialize the flat input - _input_flattened.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten)); - - // Note that the "other cases" include both: - // 1. first_n_reduce_axes < 3: Reduce the first 1 (no need to reduce) or 2 dimensions (inclusive) - // 2. first_n_reduce_axes == 4: Reduce all 4 dimensions. This can only be handled by NEReshapeKernel instead of NEFlattenKernel. - if(first_n_reduce_axes == 3) - { - auto flatten_kernel_ptr = support::cpp14::make_unique<NEFlattenLayer>(); - flatten_kernel_ptr->configure(input, &_input_flattened); - _flat_or_reshape_ptr = std::move(flatten_kernel_ptr); - } - else - { - auto reshape_kernel_ptr = support::cpp14::make_unique<NEReshapeLayer>(); - reshape_kernel_ptr->configure(input, &_input_flattened); - _flat_or_reshape_ptr = std::move(reshape_kernel_ptr); - } - - // We need to init the output tensor here. Indeed, the reshape kernel expects - // both tensors to be already initialized - auto_init_if_empty(*output->info(), *input->info()->clone()); -} - -template <bool IS_LOG> void NESoftmaxLayerGeneric<IS_LOG>::configure(ITensor *input, ITensor *output, float beta, int32_t axis) { // Perform validation step ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_ERROR_THROW_ON(NESoftmaxLayerGeneric::validate(input->info(), output->info(), beta, axis)); - // Convert reduce-before axis (inclusive) to first n axes to reduce - size_t first_n_reduce_axes = dim_index_2_num_dims(axis, static_cast<int32_t>(input->info()->num_dimensions())); + const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(input->info()->num_dimensions()))); - // We only need flattening when the number of axes to reduce is greater than 1 - _needs_flattening = first_n_reduce_axes > 1; + _needs_permute = actual_axis > 0; - // If we are dealing with a 4D tensor, we will: - // - Flatten the input, so that we end up with a [width*height*depth] * batches 2D tensor - // - Execute all the pipeline (reduction + normalization) on the flattened tensor - // - Reshape the flattened output into the real output - if(_needs_flattening) + if(_needs_permute) { - // Add to the memory manager _input_flattened - _memory_group.manage(&_input_flattened); + // Add to the memory manager _input_permuted + _memory_group.manage(&_input_permuted); - // Configure _flatten_kernel and _input_flattened - configure_reshape_input_kernel(input, output, first_n_reduce_axes); + _permute_input.configure(input, &_input_permuted, get_permutation_vector_from_softmax_axis(actual_axis)); } - // We want to deal with a 2D input. Either it is the flattened version of the original input (4D case) + // We want to deal with a 2D input. Either it is the permuted version of the original input (4D case) // or it is the original input case (2D case) - ITensor *input_2D = (_needs_flattening ? &_input_flattened : input); + ITensor *tmp_input = (_needs_permute ? &_input_permuted : input); // Create intermediate tensors shapes - const TensorInfo input_info = input_2D->info()->clone()->reset_padding().set_is_resizable(true); - DataType tmp_data_type = is_data_type_quantized_asymmetric(input_2D->info()->data_type()) ? DataType::F32 : input_2D->info()->data_type(); + const TensorInfo input_info = tmp_input->info()->clone()->reset_padding().set_is_resizable(true); + DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input->info()->data_type()) ? DataType::F32 : tmp_input->info()->data_type(); TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type)); // Init intermediate tensors - TensorShape max_sum_shape = input_2D->info()->tensor_shape(); + TensorShape max_sum_shape = tmp_input->info()->tensor_shape(); max_sum_shape.set(0, 1); _max.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape)); _tmp.allocator()->init(tensor_info_tmp); @@ -113,27 +76,27 @@ void NESoftmaxLayerGeneric<IS_LOG>::configure(ITensor *input, ITensor *output, f _memory_group.manage(&_tmp); // Configure Kernels - _max_kernel.configure(input_2D, &_max); - if(_needs_flattening) + _max_kernel.configure(tmp_input, &_max); + if(_needs_permute) { - // Add to the memory manager _output_flattened - _memory_group.manage(&_output_flattened); + // Add to the memory manager _output_permuted + _memory_group.manage(&_output_permuted); - // The normalization kernel stores the result in a flat output tensor - _softmax_kernel.configure(input_2D, &_max, &_output_flattened, beta, &_tmp); - _input_flattened.allocator()->allocate(); + // The normalization kernel stores the result in a permuted output tensor + _softmax_kernel.configure(tmp_input, &_max, &_output_permuted, beta, &_tmp); + _input_permuted.allocator()->allocate(); - // Reshape the flat output into the requested (4D) output - _reshape.configure(&_output_flattened, output); + // Re-permute the permuted output into the requested (4D) output + _permute_output.configure(&_output_permuted, output, get_permutation_vector_from_softmax_axis(actual_axis)); - // Allocate the intermediate flat tensors - _output_flattened.allocator()->allocate(); + // Allocate the intermediate permuted tensors + _output_permuted.allocator()->allocate(); } else { // Softmax 2D case - _fill_border_kernel.configure(input_2D, _max_kernel.border_size(), BorderMode::REPLICATE); - _softmax_kernel.configure(input_2D, &_max, output, beta, &_tmp); + _fill_border_kernel.configure(tmp_input, _max_kernel.border_size(), BorderMode::REPLICATE); + _softmax_kernel.configure(tmp_input, &_max, output, beta, &_tmp); } // Allocate intermediate buffers @@ -148,12 +111,8 @@ Status NESoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const I ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 4, "Only up to 4 dimensions are supported"); ARM_COMPUTE_UNUSED(beta); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis != 0, "Only axis 0 supported"); ARM_COMPUTE_RETURN_ERROR_ON(axis < static_cast<int32_t>(-input->num_dimensions()) || static_cast<int32_t>(input->num_dimensions()) <= axis); - // Convert reduce-before axis (inclusive) to first n axes to reduce - size_t first_n_reduce_axes = dim_index_2_num_dims(axis, static_cast<int32_t>(input->num_dimensions())); - // Create intermediate tensor info DataType tmp_data_type = input->data_type(); const TensorInfo tensor_info_tmp(input->clone()->set_data_type(tmp_data_type).set_is_resizable(true)); @@ -163,21 +122,18 @@ Status NESoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const I const TensorInfo tensor_info_max_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(input->quantization_info()).set_is_resizable(true)); const TensorInfo dont_care; - const bool needs_flattening = (first_n_reduce_axes > 1); + const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(input->num_dimensions()))); + + const bool needs_permute = actual_axis > 0; - if(needs_flattening) + if(needs_permute) { - const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input, first_n_reduce_axes); - TensorInfo tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true)); - - if(first_n_reduce_axes == 3) - { - ARM_COMPUTE_RETURN_ON_ERROR(NEFlattenLayer::validate(input, &tensor_info_flat)); - } - else - { - ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayer::validate(input, &tensor_info_flat)); - } + const PermutationVector permutation_vector = get_permutation_vector_from_softmax_axis(actual_axis); + const TensorShape permuted_shape = misc::shape_calculator::compute_permutation_output_shape(*input, permutation_vector); + TensorInfo input_permuted(input->clone()->set_tensor_shape(permuted_shape)); + ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(input, &input_permuted, permutation_vector)); + TensorInfo output_permuted(output->clone()->set_tensor_shape(permuted_shape)); + ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(&output_permuted, output, permutation_vector)); } ARM_COMPUTE_RETURN_ON_ERROR(NELogits1DMaxKernel::validate(input, &tensor_info_max_sum)); @@ -191,18 +147,18 @@ void NESoftmaxLayerGeneric<IS_LOG>::run() { MemoryGroupResourceScope scope_mg(_memory_group); - if(_needs_flattening) + if(_needs_permute) { - _flat_or_reshape_ptr->run(); + _permute_input.run(); } NEScheduler::get().schedule(&_fill_border_kernel, Window::DimY); NEScheduler::get().schedule(&_max_kernel, Window::DimY); NEScheduler::get().schedule(&_softmax_kernel, Window::DimY); - if(_needs_flattening) + if(_needs_permute) { - _reshape.run(); + _permute_output.run(); } } |